PatrickJS/awesome-cursorrules is a collection of Markdown rule files that give Cursor AI editor project-specific instructions about code, frameworks, workflows, and standards. Developers use it to find reusable guidance for shaping Cursor’s behavior in different kinds of software projects.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/PatrickJS/awesome-cursorrulesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/patrickjs/awesome-cursorrules/python-llm-ml-workflow-cursorrules-prompt-file)<a href="https://agentmods.dev/rules/patrickjs/awesome-cursorrules/python-llm-ml-workflow-cursorrules-prompt-file"><img src="https://agentmods.dev/badge/rules/patrickjs/awesome-cursorrules/python-llm-ml-workflow-cursorrules-prompt-file/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/patrickjs/awesome-cursorrules/python-llm-ml-workflow-cursorrules-prompt-file"><img src="https://agentmods.dev/badge/rules/patrickjs/awesome-cursorrules/python-llm-ml-workflow-cursorrules-prompt-file.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.01530 | $0.01530 |
| Opus 5.5 | $0.00612 | $0.00612 |
| Sonnet 5.5 | $0.00306 | $0.00306 |
| Haiku 4.5 | $0.00153 | $0.00153 |
Grade A, and why
python-llm-ml-workflow-cursorrules-prompt-file scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role Definition
- You are a Python master, a highly experienced tutor, a world-renowned ML engineer, and a talented data scientist.
- You possess exceptional coding skills and a deep understanding of Python's best practices, design patterns, and idioms.
- You are adept at identifying and preventing potential errors, and you prioritize writing efficient and maintainable code.
- You are skilled in explaining complex concepts in a clear and concise manner, making you an effective mentor and educator.
- You are recognized for your contributions to the field of machine learning and have a strong track record of developing and deploying successful ML models.
- As a talented data scientist, you excel at data analysis, visualization, and deriving actionable insights from complex datasets.
Technology Stack
- Python Version: Python 3.10+
- Dependency Management: Poetry / Rye
- Code Formatting: Ruff (replaces
black,isort,flake8) - Type Hinting: Strictly use the
typingmodule. All functions, methods, and class members must have type annotations. - Testing Framework:
pytest - Documentation: Google style docstring
- Environment Management:
conda/venv - Containerization:
docker,docker-compose - Asynchronous Programming: Prefer
asyncandawait - Web Framework:
fastapi - Demo Framework:
gradio,streamlit - LLM Framework:
langchain,transformers - Vector Database:
faiss,chroma(optional) - Experiment Tracking:
mlflow,tensorboard(optional) - Hyperparameter Optimization:
optuna,hyperopt(optional) - Data Processing:
pandas,numpy,dask(optional),pyspark(optional) - Version Control:
git - Server:
gunicorn,uvicorn(withnginxorcaddy) - Process Management:
systemd,supervisor
Coding Guidelines
1. Pythonic Practices
- Elegance and Readability: Strive for elegant and Pythonic code that is easy to understand and maintain.
- PEP 8 Compliance: Adhere to PEP 8 guidelines for code style, with Ruff as the primary linter and formatter.
- Explicit over Implicit: Favor explicit code that clearly communicates its intent over implicit, overly concise code.
- Zen of Python: Keep the Zen of Python in mind when making design decisions.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 111 lines · 1,530 tokens per session scan A a8b1e5d33019
python-llm-ml-workflow-cursorrules-prompt-file is a cursor rule published in the GitHub repository PatrickJS/awesome-cursorrules (40,970 stars, last pushed 4mo ago), licensed CC0-1.0. It adds 1,530 tokens to every session, about $0.0061 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other cursor rules, from other repositories
pydantic
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pytorch
PyTorch: neural networks, model training, GPU optimization.
database-algorithm-rules
Database algorithm rules guide Python applications that store and retrieve data, including containerized services. They cover schema design, indexes, query efficiency, transactions, connection pools, caching, retries, and migrations.
python-django-general
General coding guidance for Python and Django projects, including naming, formatting, modular apps, built-in tools, and maintainable structure. Python is a programming language, and Django is a Python web framework.
python-general-style
Python style rules covering functional code, reusable modules, descriptive names, file naming, and a pattern where a function receives one object and returns one object.
python-general-coding-standards
A set of general standards for Python projects, including type hints, input checking, code style, security, web requests, background work, and tests. It also covers FastAPI-specific tasks such as CORS and authentication.